| Optimal approximation with sparsely connected deep neural networks H Bolcskei, P Grohs, G Kutyniok, P Petersen SIAM Journal on Mathematics of Data Science 1 (1), 8-45, 2019 | 276 | 2019 |
| A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations P Grohs, F Hornung, A Jentzen, P Von Wurstemberger Memoirs of the American Mathematical Society, 2018 | 229* | 2018 |
| Deep neural network approximation theory D Elbrächter, D Perekrestenko, P Grohs, H Bölcskei IEEE Transactions on Information Theory, 2019 | 206 | 2019 |
| Analysis of the generalization error: Empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of … J Berner, P Grohs, A Jentzen SIAM Journal on Mathematics of Data Science 2 (3), 631-657, 2020 | 178 | 2020 |
| The modern mathematics of deep learning J Berner, P Grohs, G Kutyniok, P Petersen arXiv preprint arXiv:2105.04026, 86-114, 2021 | 134* | 2021 |
| Solving stochastic differential equations and Kolmogorov equations by means of deep learning C Beck, S Becker, P Grohs, N Jaafari, A Jentzen arXiv preprint arXiv:1806.00421 1 (1), 2018 | 125 | 2018 |
| DNN expression rate analysis of high-dimensional PDEs: Application to option pricing D Elbrächter, P Grohs, A Jentzen, C Schwab Constructive Approximation 55 (1), 3-71, 2022 | 116 | 2022 |
| Laguerre minimal surfaces, isotropic geometry and linear elasticity H Pottmann, P Grohs, NJ Mitra Advances in computational mathematics 31, 391-419, 2009 | 87 | 2009 |
| Parabolic molecules P Grohs, G Kutyniok Foundations of Computational Mathematics 14, 299-337, 2014 | 82 | 2014 |
| Solving the Kolmogorov PDE by means of deep learning C Beck, S Becker, P Grohs, N Jaafari, A Jentzen Journal of Scientific Computing 88, 1-28, 2021 | 74 | 2021 |
| Stable phase retrieval in infinite dimensions R Alaifari, I Daubechies, P Grohs, R Yin Foundations of Computational Mathematics 19, 869-900, 2019 | 70 | 2019 |
| ε-subgradient algorithms for locally lipschitz functions on Riemannian manifolds P Grohs, S Hosseini Advances in Computational Mathematics 42, 333-360, 2016 | 70 | 2016 |
| Continuous shearlet frames and resolution of the wavefront set P Grohs Monatshefte für Mathematik 164 (4), 393-426, 2011 | 70 | 2011 |
| Phase retrieval: uniqueness and stability P Grohs, S Koppensteiner, M Rathmair SIAM Review 62 (2), 301-350, 2020 | 69 | 2020 |
| Group testing for SARS-CoV-2 allows for up to 10-fold efficiency increase across realistic scenarios and testing strategies CM Verdun, T Fuchs, P Harar, D Elbrächter, DS Fischer, J Berner, ... Frontiers in Public Health 9, 583377, 2021 | 60 | 2021 |
| Phase retrieval in the general setting of continuous frames for Banach spaces R Alaifari, P Grohs SIAM journal on mathematical analysis 49 (3), 1895-1911, 2017 | 58 | 2017 |
| α-Molecules P Grohs, S Keiper, G Kutyniok, M Schäfer Applied and Computational Harmonic Analysis 41 (1), 297-336, 2016 | 53 | 2016 |
| Smoothness properties of Lie group subdivision schemes J Wallner, EN Yazdani, P Grohs Multiscale modeling & simulation 6 (2), 493-505, 2007 | 52 | 2007 |
| Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions P Grohs, L Herrmann IMA Journal of Numerical Analysis 42 (3), 2055-2082, 2022 | 51 | 2022 |
| Stable Gabor phase retrieval and spectral clustering P Grohs, M Rathmair Communications on Pure and Applied Mathematics 72 (5), 981-1043, 2019 | 51 | 2019 |
